丁 院士，北京大学化学与分子工程学院教授。专注于中国社会能源和资源利用的优化，其主要研究涵盖氢气的生产与运输、废塑料转化、C1化学以及催化反应机理的研究。曾获2013年北京大学青年教师教学竞赛一等奖，并于2018年被评为北京大学十佳导师之一。担任ACS Catalysis、Journal of Catalysis和Catalysis Science & Technology的副编辑及编委会成员，并担任故宫博物院文化遗产传承实验室学术委员会成员。
## 讲座简介
Understanding the interfacial catalytic behavior through in-depth mechanism research is crucial for applying artificial intelligence and bigdata methods to design efficient catalysts. The application of artificial intelligence (AI) and big data methods to heterogeneous catalyst design resents both unique opportunities and challenges. These difficulties mainly arise from the challenge of accurate data labeling—a critical step in training AI models. Unlike in fields such as Go game playing or protein structure prediction where clear labeling and training targets can be provided through definite game outcomes or known protein structures), catalyst design, especially heterogeneous catalyst design, involves complex and often ambiguous precise structures and interactions at the atomic or molecular level. Particularly, catalyst structures often exhibit a distribution, and their possible restructuring under reaction conditions further increases the difficulty of applying AI and big-data methods to catalyst design.
In application areas such as Go game playing or protein structure prediction, AI systems like AlphaGo and AlphaFold have achieved remarkable success. AlphaGo learns to predict game outcomes by studying numerous games with clear win/loss criteria, whereas the accuracy of AlphaFold in protein structure prediction benefits from the well-defined nature of three-dimensional protein structures. In these cases, AI systems are trained on massive, high-quality, unambiguous data, enabling highly precise and deterministic learning. In contrast, heterogeneous catalyst design, especially under actual operating conditions, faces a completely different scenario. Take the widely studied Cu-Zn-Al catalyst for methanol synthesis as an example. Despite extensive literature, there are still gaps in understanding the structure of active sites under real high-temperature and high-pressure reaction conditions. Although most catalyst systems have been thoroughly studied, the actual active structures and their dynamic behaviors during the reaction process remain unknown at the atomic level. Yet the training of algorithms capable of accurately predicting catalytic behavior and performance relies on precisely labeled datasets, and this knowledge gap severely limits the effectiveness of AI methods in catalyst design. In this report, we use the catalytic oxidation of H2 and CO over Pt/CeO2 catalysts as an example to illustrate the complexity of heterogeneous catalysis and the importance of mechanistic studies.
Professor German A. Prieto is an Associate Professor in the Department of Geosciences at the Universidad Nacional de Colombia. His research focuses on earthquake source physics and the ground motions observed at Earth's surface. He earned his PhD in Earth Sciences from UC San Diego, completed a Thompson Postdoctoral Fellowship at Stanford University, and previously served on the faculty at MIT. His work develops advanced seismological analysis methods to study earthquake source processes, Earth structure, and their effects on ground motions, including contributions to regional tomography, wave propagation and scattering, and ambient-noiseseismology. He is curenty an Editor for Geophysical Research Letters and has held various editorial and commitee roles within AGU. His honors include the Keiti Aki Young Scientist Award and an Editors Citation for Excellence in Refereeing from JGR: Solid Earth.
Professor Germán A. Prieto is an Associate Professor in the Department of Geosciences at the Universidad Nacional de Colombia. His research focuses on earthquake source physics and the ground motions observed at Earth's surface. He earned his PhD in Earth Sciences from UC San Diego, completed a Thompson Postdoctoral Fellowship at Stanford University, and previously served on the faculty at MIT. His work develops advanced seismological analysis methods to study earthquake source processes, Earth structure, and their effects on ground motions, including contributions to regional tomography, wave propagation and scattering, and ambient-noiseseismology. He is currectly an Editor for Geophysical Research Letters and has held various editorial and commitee roles within AGU. His honors include the Keiti Aki Young Scientist Award and an Editors Citation for Excellence in Refereeing from JGR: Solid Earth.
## 讲座简介
The initiation of subduction zones is a poorly understood but core plate tectonic process. One unknown is how and under what conditions previously contiguous plates break. Here we present a comprehensive assessment of the Northern Panama Subduction Zone (NPSZ) where the Caribbean Plate is subducting to the SSW beneath Panama. Because the Panama arc was built onto the Caribbean Plate, the existence of the NPSZ means that the Caribbean Plate must have broken. The significant crustal thickness (~20 km) and age (>90 Ma) of the Caribbean Plate make the NPSZ the closest analog to passive margin failure known. We use evidence from the 25 May 2023 Mw 6.3 megathrust earthquake northeast of Panama included in a newly refined earthquake catalog to identify the geometry of the downgoing plate. We combine these with plate reconstructions to study the initiation of the NPSZ, its subsequent evolution, and its tectonic implications.
Prof. Licheng Sun received his PhD degree in 1990 from Dalian University of Technology (DUT), and went to Germany as a postdoc at Max-Planck-Institut für Strahlenchemie with Dr. Helmut Görner (1992-1993), and then as an Alexander von Humboldt postdoc at Freie Universität Berlin (1993-1995) with Prof. Dr. Harry Kurreck. He moved to KTH Royal Institute of Technology, Stockholm, in 1995 as a postdoc with Prof. Björn Åkemark, became an assistant professor in 1997 at KTH, an associate professor in 1999 at the Department of Organic Chemistry, Stockholm University, and a full professor in 2004 at the Department of Chemistry, KTH. In 2018, he became the national distinguished professor of the Swedish Research Council (VR Rådsprofessor). He is currently a chair professor at Westlake University, China, and director of the Center of Artificial Photosynthesis for Solar Fuels at Westlake University.
Prof. Licheng Sun received his PhD degree in 1990 from Dalian University of Technology (DUT), and went to Germany as a postdoc at Max-Planck-Institut für Strahlchemie with Dr. Helmut Görner (1992-1993), and then as an Alexander von Humboldt postdoc at Freie Universität Berlin (1993-1995) with Prof. Dr. Harry Kurreck. He moved to KTH Royal Institute of Technology, Stockholm, in 1995 as a postdoc with Prof. Björn Åkemark, became an assistant professor in 1997 at KTH, an associate professor in 1999 at the Department of Organic Chemistry, Stockholm University, and a full professor in 2004 at the Department of Chemistry, KTH. In 2018, he became the national distinguished professor of the Swedish Research Council (VR Rådsprofessor). He is currently a chair professor at Westlake University, China, and director of the Center of Artificial Photosynthesis for Solar Fuels at Westlake University.
His research interests cover artificial photosynthesis, solar cells and solar fuels, including dye-sensitized solar cells, perovskite solar cells, bio-inspired catalysts for water oxidation and hydrogen generation, nanomaterials and photoelectrochemical cells for water splitting, anion-exchange membrane water electrolysis (AEM-WE), CO2 reduction, and N2 fixation. In particular, Prof. Sun has made an outstanding contribution to the design and synthesis of molecular catalysts and material catalysts with well-defined active site structures for water oxidation with the catalytic efficiency comparable to the OEC (oxygen evolution complex) in Photosystem II, and the deep insight studies on the reaction mechanisms of O-O bond formation in both natural and man-made photosynthetic systems.
